One of the most challenging threats to date is cybercrime. Today, cybercriminals adapt to the newest tactics to scam retailers, consumers, and businesses with an increase in online businesses. Cybercrime refers to stealing confidential data or money via various devices, including computers or smartphones, by digital means. Cybercrime involves a wide range of illicit activities such as cyber theft, cyber trespass, cyberstalking, etc. It includes stealing data, but sometimes cybercriminals infect a victim’s site with a virus to damage or disrupt their everyday operations. Although, in most cases, the intention behind this illegal activity is financial, it may also be because of personal or political agendas.
Previously, organizations followed rigorous methods to manually assess and analyze fraud by employing a human workforce. Identifying scams using this method required plenty of time, and the probability of inaccuracy was higher. An increased number of false positives could lead to trust issues and another investigation. Today, each industry is looking for methodologies to identify, prevent, and eliminate such forgeries using advanced algorithms and methods.
Artificial Intelligence (AI) is the best possible solution to thwart fraud and eliminate chances of false transactions, thus improving user experience. Before diving in to learn AI’s role in blocking fraudulent activities, individuals must understand what artificial intelligence is. Artificial Intelligence is a growing phenomenon and defines the ability of a computer or any smart machine to act like humans. AI executes in-depth data analysis and detects frauds that many fraud analysts cannot perform at a large scale. By implementing AI algorithms, the workload of fraud analysts reduces, simultaneously increasing their efficiency. AI promptly notifies fraud analysts in large enterprises about any potential fraudulent activity.
Undoubtedly, defrauders are always active. The fraudulent activities did not even stop at the time of the global pandemic. Due to the rising number of fraud cases, larger enterprises are recruiting employees to develop and implement AI software. For this reason, individuals are stepping up their game and considering online learning to broaden their employment opportunities. Nonetheless, there are several factors to bear in mind when implementing AI systems. Let’s have a look at them briefly;
Points To Consider When Using AI
- Integration of Supervised and Unsupervised Machine Learning
Crimes these days are so advanced and refined, leaving almost zero traces. With that said, defense mechanisms that still use a standardized approach to detect fraudulent activities are no longer credible. Instead, there must be a creatively crafted system that detects aberrations for each fraud case scenario and serves as a perfect solution for the given case. Therefore, AI models are now incorporating both supervised and unsupervised machine learning for forgery identification.
Supervised machine learning usually learns the past trends of data that have been labeled as either fraudulent or non-fraudulent. The model uses past data and applies it to the existing one to identify whether the behavior was legitimate or illegitimate. In contrast, unsupervised machine learning identifies fraud when there is a lack of tagged data. It implies that the model has to self-learn and detect the behavior without pre-information since it is indiscernible to other analysis tools.
Combining both of these techniques makes it possible to recognize unusual patterns and detect potential fraud scenarios.
- Take Advantage of Large Datasets
Studies have revealed that the AI model works more effectively if there is an extensive database. Even the algorithm of the model is not as robust as large datasets. Take it this way: the more healthcare workers assess patients and diagnose diseases, the more they gain experience. It is the same case with fraud identification models. The more data is fed into the model, the more experience it will gain by examining billions and trillions of data containing suspicious patterns and scam activities. With the recent developments in cybercrime, it becomes indispensable to include comprehensive examples of fraudulent activity. With these examples, AI becomes stringent at identifying similar activities in the future.
The AI model assesses a broad range of data to achieve exceptional fraud detection results and learn about various behavior and risks. It means that to achieve high accuracy, the expansion and diversification of data will play an essential role.
- Benefit from Real-Time Analysis
The latest technology has been providing solutions for combating forgery for a while. However, artificial intelligence is taking fraud detection and prevention to the next level. AI models are now looking for ways to detect fraud even before it happens. Usually, chargebacks would arise weeks after the fraud occurs. Many organizations are employing advanced artificial intelligence technologies to detect fraud in less than a second. Since fraudulent transactions include many parameters, including name, location, transaction amount, etc., AI examines all these parameters to detect fraud or any abnormality in real-time.
Types of Fraudulent Activities and How AI Can Prevent Them
Below are some of the fraud scenarios and how AI helps in eradicating such scams:
- Email Phishing
Email phishing is the fastest way for defrauders to rob users of their data. In this type of cybercrime, scammers send fake website links and messages to the recipients to collect their data. They use confidential data to hack into the recipients’ systems and steal their money or personal data. It might seem easy to ignore such scam emails; however, the truth is these defrauders are smart. It can be challenging to distinguish which email is authentic or a scam.
Artificial Intelligence reviews the email details, including the sender’s email address, email subject, and content. In this way, AI can detect whether the email is a scam and automatically prevents access by separating it from authentic emails.
- Identity Theft
Professional criminals will steal personal data such as your login details for various sites or applications in this type of fraudulent activity. Later on, these fraudsters will change these login details, blocking users from using their accounts. AI expeditiously identifies the hacking and notifies the user. However, it is only possible if organizations maintain an ordered and systemized data.
Here, hijackers are intelligent enough to understand how a web page’s security system functions. For example, these hackers will steal website users’ credit card details while they enter them. It usually happens with poorly coded websites. Artificial Intelligence provides security to the website and ensures attackers don’t have access to the users’ confidential information.
Artificial intelligence serves as a concrete foundation for fraud detection systems and preventing corrupt practices. AI can save a great deal of money and time and sometimes also mitigate the chances of damages before it occurs. Many organizations now understand the need and importance of AI in their financial systems. However, they also need to understand the significance of continuously updating and managing the AI tools they implement. Suppose organizations don’t keep themselves updated with the concept of AI and fail to implement it correctly. In that case, unfortunately, the defense strategies will lack effectiveness and will continue to work on one size fits all approach